Anticipating and monitoring water risks for agriculture

Annex A. Tools fiches

This annex contains examples of tools that aggregate analysed data to support decisions by monitoring and anticipating water risks. Tools marked with an asterisk (*) are still in experimental phase and may not yet be fully deployed. Other tools are operational and established tools, already in use.

Real-time hazard monitoring and short-term forecast tools

Water excess

Table A A.1. GloFAS Global Flood Monitoring

Developed by	Copernicus Emergency Management Service
Type(s) of physical water risk covered	Water excess (flood) Blue water
Dimensions of risk included	Hazard
Purpose	Near real-time global flood monitoring
Intended user	Disaster emergency managers, river basin managers, environment/water agencies, ministries of agriculture/water/environment, international development/aid agencies.
Format	GloFAS map viewer online platform
Data source	Immediate processing of information from the SENTINEL-1 satellite through water and flood mapping algorithms to provide flood and water extent maps in a fully automated system.
Methodology/technology	Modelling using algorithms, remote sensing: Integrated real-time precipitation information from multiple satellites into a quasi-global hydrological runoff and routing model
Temporal scale	Near-real tine
Update frequency	N/A
Spatial resolution	10 m
Geographical coverage	Global
Limitations and caveats	The tool indicates where the quality of flood mapping may be reduced due to environmental conditions (e.g. dry soil influencing flood dynamics) or degraded data input quality.

Source: (Copernicus Emergency Management Service, n.d.[119]).

Table A A.2. Global Flood Monitoring System*

Developed by	University of Maryland (funded by NASA)
Type(s) of physical water risk covered	Water excess (flood) Blue water
Dimensions of risk included	Hazard – flood detection and intensity (flood levels) mapped
Purpose	Anticipation: short-term (4‑5 day) flood forecasting Real-time monitoring
Intended user	Disaster emergency managers, river basin managers, environment/water agencies, ministries of agriculture/water/environment, insurers, international development/aid agencies
Format	Web-based map
Data source	Integrated real-time precipitation information from multiple satellites into a quasi-global hydrological runoff and routing model, international organisations
Methodology/technology	Modelling using algorithms and remote sensing
Temporal scale	Short-term (4-5 days)
Update frequency	Real time
Spatial resolution	Inundation, streamflow and surface water storage are calculated with a 1km resolution
Geographical coverage	Quasi-global (50°N - 50°S)
Limitations and caveats	The tool is in experimental phase.

Source: (University of Maryland, n.d.[120]).

Water shortage

Table A A.3. Global Drought Observatory (GDO)

Developed by	Copernicus Emergency Management System/ Joint Research Centre
Type(s) of physical water risk covered	Water shortage (drought) Blue and Green water
Dimensions of risk included	Forecast and monitor drought hazard Risk of impact assessed by sector, based on hazard, exposure and vulnerability.
Purpose	Anticipation (forecasting) and monitoring
Intended user	Disaster emergency managers, river basin/irrigation managers, environment/water agencies, ministries of agriculture/water/environment, international development/aid agencies, insurers, extension officers…
Format	Interactive, publicly available, web-based map and analytical reports
Data source	Precipitation measurements, satellite measurements, modelled soil moisture content
Methodology/technology	The Combined Drought Indicator (CDI) for agricultural/ecosystem drought is calculated based on three indicators: Precipitation Anomalies (SPI), Soil Moisture Anomalies and Fraction of Absorbed Photosynthetically Active Radiation (fAPAR) A Risk of Drought Impact for Agriculture (RDrI-Agri) index is calculated, showing the probability of having drought impacts, particularly for vegetation, combining hazard (combination of precipitation anomaly (SPI), anomaly of photosynthetic activity (fAPAR) and soil moisture anomalies) exposure (of population, livelihoods and assets) and vulnerability.
Temporal scale	Monitoring: near real time Forecasting: one, three and six months
Update frequency	Every 10 days
Spatial resolution	1 km
Geographical coverage	Global
Limitations and caveats	N/A

Source: (JRC, n.d.[121]), (United Nations, n.d.[122]), (Vogt, 2019[123]).

Table A A.4. European Drought Observatory (EDO)

Developed by	Copernicus Emergency Management System/ Joint Research Centre
Type(s) of physical water risk covered	Water shortage (drought) Blue (e.g. reservoir levels) and green water (e.g. soil moisture)
Dimensions of risk included	Short-term forecasting and monitoring of drought hazard Risk of impact assessed by sector, based on hazard, exposure and vulnerability.
Purpose	Near-real time monitoring and early warning of drought Forecasting of unusually dry (and wet) conditions
Intended user	Disaster emergency managers, river basin/irrigation managers, environment/water agencies, ministries of agriculture/water/environment, insurers, extension officers
Format	Interactive, publicly available, web-based map and analytical reports
Data source	Precipitation measurements, satellite measurements, modelled soil moisture content
Methodology/technology	The EDO provides the following indicators: Standardized Precipitation Index (SPI), Standardized Snowpack Index (SSPI), Soil Moisture Anomaly (SMA), Anomaly of Vegetation Condition (FAPAR Anomaly), Low-Flow Index (LFI), Heat and Cold Wave Index (HCWI), Combined Drought Indicator (CDI). The Combined Drought Indicator is calculated integrating precipitation (standardized precipitation index, SPI), soil moisture (soil moisture anomaly, SMA), vegetation health (fraction of the absorbed photosynthetically active radiation, FAPAR) The CDI interprets drought as a cascade process: a precipitation shortage (WATCH stage) may develop into a soil water deficit (WARNING stage), leading to stress for vegetation (ALERT stage).
Temporal scale	Monitoring: near real time Forecasting: one, three and six months
Update frequency	Every 10 days
Spatial resolution	5 km
Geographical coverage	Europe and part of North Africa
Limitations and caveats	N/A

Source: (Cammalleri et al., 2021[124]), (Copernicus, n.d.[125]) (EEA, n.d.[126]) (JRC, n.d.[121]).

Table A A.5. Near-real Time Meteorological Drought Monitoring and Early Warning System for Croplands in Asia

Developed by	The University of Tokyo’s Institute of Industrial Science
Type(s) of physical water risk covered	Water shortage (drought) Blue and green water (evapotranspiration)
Dimensions of risk included	Drought hazard and impact on croplands
Purpose	Near-real time monitoring and early warning of drought Forecasting of unusually dry (and wet) conditions
Intended user	Agriculture Ministries in the Asia Pacific Region Ministries of Environment/Water, disaster emergency managers, river basin/irrigation managers, environment/water agencies, extension officers, insurers, international development and aid agencies
Format	Interactive web-based map and drought warning provided to public authorities
Data source	Remote sensing: satellite-based measurement of temperature, rainfall and vegetation
Methodology/technology	The Keeth–Byram Drought Index calculated from satellite-based rainfall, temperature and vegetation phenology data
Temporal scale	Near real time monitoring and short-term early warning
Update frequency	Daily
Spatial resolution	N/A
Geographical coverage	Asia Pacific
Limitations and caveats	N/A

Source: (Institute of Industrial Science, n.d.[127]), (Takeuchi et al., 2015[128]).

Table A A.6. South Asia Drought Monitoring System

Developed by	IWMI
Type(s) of physical water risk covered	Water shortage (drought) Includes blue and green water (soil moisture)
Dimensions of risk included	Hazard – drought severity is monitored, including for agricultural drought.
Purpose	Monitoring and anticipation, through early warning
Intended user	Public authorities
Format	Interactive web-based tool, with map
Data source	Integrates remote sensing (moderate resolution imaging spectroradiometer (MODIS) and tropical rainfall measuring mission (TRMM), ESA Soil Moisture (ASCAT) Products) and ground truth data (vegetation indices, rainfall data, soil information, hydrological data)
Methodology/technology	Modelling and remote sensing Drought severity is calculated based on the soil moisture index (SMI), which is derived from a hydrological model considering observed meteorological data, morphological variables and ecological processes. The SMI is classified into five drought severity levels adopted from the US Drought Monitor scaling.
Temporal scale	Short term (near real time monitoring)
Update frequency	Daily (latency of five days)
Spatial resolution	High (0.25∘), regional and local levels
Geographical coverage	Afghanistan, Bangladesh, Bhutan, India, Nepal, Pakistan and Sri Lanka
Limitations and caveats	N/A
Additional notes	IWMI received the Geospatial World Excellence Award for its innovative work using remote sensing technology to help nations monitor and mitigate the impacts of drought.

Source: (Saha et al., 2021[129]).

Table A A.7. US Drought Monitor

Developed by	National Drought Mitigation Center at the University of Nebraska-Lincoln, the National Oceanic and Atmospheric Administration and the U.S. Department of Agriculture
Type(s) of physical water risk covered	Water shortage (drought) Blue and green water (e.g. soil moisture)
Dimensions of risk included	Drought hazard and impact
Purpose	Monitoring
Intended user	Ministry of Agriculture: The United States Department of Agriculture and its agencies use it to trigger drought warning, disaster declarations and determine farmers’ eligibility to low interest loans and other support programmes. Local decision makers for drought response measures, river basin and irrigation managers, farmers, extension officers, insurers
Format	Interactive web-based format with maps
Data source	Observed meteorological and hydrological data, satellite data and expert judgement
Methodology/technology	Drought indices are calculated (e.g. Palmer Drought Index, Standardized Precipitation Index) and combined with expert judgement, a map indicating drought impacted areas is drawn. Drought-impacted areas are classified in a five-category system (from “abnormally dry”, i.e. 1-to-3-year drought events, to exceptionally dry, i.e. 1 to 50 year drought events), defining the severity, spatial extent and impacts of the drought.
Temporal scale	One week
Update frequency	Weekly
Spatial resolution	N/A
Geographical coverage	United States
Limitations and caveats	N/A
Additional notes	Together with the Canadian Drought Monitor and the Mexico Drought Monitor, it forms the North American Drought Monitor (NADM). The NADM is a co‑operative effort between drought experts of the three countries. Maps from the North American continent are published monthly, using the same five category classification system.

Source: (NOAA, n.d.[130]), (Government of Canada, n.d.[131]), (National Drought Mitigation Center, 2025[132]).

Table A A.8. Canadian Drought Monitor

Developed by	Agriculture and Agri-Food Canada
Type(s) of physical water risk covered	Water shortage (drought) Blue and green water (e.g. soil moisture)
Dimensions of risk included	Drought hazard and impact
Purpose	Monitoring
Intended user	Ministry of Agriculture, Water, Environment (The Government of Canada, including Agriculture and Agri-Food Canada uses this tool to monitor drought), environment/ water agencies, river basin/irrigation managers, disaster emergency managers, farmers, extension officers, insurers
Format	Interactive web-based format with maps and animations
Data source	Observed meteorological and hydrological data, satellite data and expert judgement
Methodology/technology	Drought indices are calculated and combined with expert judgement, a map indicating drought impacted areas is drawn. Drought-impacted areas are classified in a five-category system, defining the severity, spatial extent and impacts of the drought.
Temporal scale	Current drought conditions
Update frequency	Monthly
Spatial resolution	N/A
Geographical coverage	Canada
Limitations and caveats	N/A
Additional notes	Together with the US Drought Monitor (see above) and the Mexico Drought Monitor, it forms the North American Drought Monitor (NADM). The NADM is a co‑operative effort between drought experts of the three countries. Maps from the North American continent are published monthly, using the same five category classification system. The CDM also underpins the Canadian Drought Outlook, a monthly national-scale forecasts of drought conditions.

Source: (NOAA, n.d.[130]), (Government of Canada, n.d.[131]).

Water quality

Table A A.9. GEO AquaWatch

Developed by	Group on Earth Observations (GEO) initiative.
Type(s) of physical water risk covered	Water quality (chlorophyll, algal blooms, coloured dissolved organic matter turbidity, etc.) Blue water
Dimensions of risk included	Hazard
Purpose	Satellite-derived water quality maps.
Intended user	Water resource and environmental managers, policymakers, private sector, international organisations, researchers
Format	Suite of services and resources: Satellite maps, timeseries, algorithm repositories
Data source	NASA, NOAA, Copernicus Global Land Service
Methodology/technology	Remote sensing and in-situ measurements
Temporal scale	Seasonal to monthly
Update frequency	N/A
Spatial resolution	From coarse to high resolution regional products.
Geographical coverage	Global
Limitations and caveats	Cloud cover interference
Additional notes	Integrates multiple satellites products, a coordination and service initiative

Source: (GEO Aqua Watch, 2024[133]).

Table A A.10. Digital-WATER.city ALERT system *

Developed by	Digital-WATER.city Project (EU Horizon 2020 consortium).
Type(s) of physical water risk covered	Water quality: Microbial contamination (E. coli) Blue water
Dimensions of risk included	Hazard
Purpose	Provide early warning and real-time risk assessment for wastewater reuse in agriculture.
Intended user	Municipalities, wastewater utilities, public health and environmental regulators.
Format	Integrated early warning systems: In-situ sensors & machine learning dashboard.
Data source	Microbial sensors at Milan Peschiera Borromeo Wastewater Treatment Plant
Methodology/technology	Combination of in-situ sensors and machine learning algorithms predicting E. coli concentration in real time.
Temporal scale	High frequency / real-time.
Update frequency	Continuous.
Spatial resolution	Plant level monitoring (point location at WWPT outflow).
Geographical coverage	Peschiera Borromeo WWTP, Milan, Italy.
Limitations and caveats	Pilot stage, scalability and cost considerations.
Additional notes	Supports water reuse risk management and reduces monitoring delays of culture-based E. coli tests.

Source: (DigitalWater.City, 2022[79]).

Irrigation advisory tools

Table A A.11. IRRINET / IRRIFRAME

Developed by	Emilia Romagna Regional Authorities
Type(s) of physical water risk covered	Water shortage
Dimensions of risk included	Hazard, vulnerability and exposure
Purpose	Irrigation scheduling based on crop water needs Optimise irrigation to reduce water use and energy consumption
Intended user	Farmers (12 000+ registered farms)
Format	Web platform, SMS, mobile app
Data source	Regional Weather Service, Geological Service, CER
Methodology/technology	Mathematical model integrating meteorological, soil, groundwater, and crop data to calculate water balance for individual crops that allows to refine water demand.
Temporal scale	Daily
Update frequency	Daily
Spatial resolution	Plot-level (integrated with Google Maps)
Geographical coverage	Emilia Romagna, Italy.
Limitations and caveats	Requires local calibration; limited by meteorological and soil map availability.
Additional notes	Saves approximately 90 million m³ of water annually; 20% water demand reduction; funded by public/EU funds.

Source: (IRRIFRAME, n.d.[134]), https://www.irriframe.it/Irriframe.

Table A A.12. Agroclimatic Information System for Irrigation (Sistema de Información Agroclimática para el Regadío, SiAR)

Developed by	Ministry of Agriculture, Fisheries and Food, Spain
Type(s) of physical water risk covered	Water quantity (rainfall, soil moisture)
Dimensions of risk included	Hazard, exposure and vulnerability
Purpose	Provides daily information on crop irrigation needs.
Potential users	Farmers, Water Authority
Format	Website (www.siar.es), mobile application and web GIS viewer (www.espaciosiar.es)
Data source	Meteorological data from > 500 weather stations; remote sensing data from Sentinel and Landsat-8 satellites.
Methodology/technology	Calculates reference evapotranspiration and effective precipitation based on data collected by more than 500 weather stations located in irrigated agricultural areas, along with information on crop type, irrigation method and soil characteristics. Furthermore, it enables monitoring and tracking of crop development using remote sensing. The Espacio SiAR tool combines data from field weather stations with satellite imagery to map irrigated areas and estimate their annual and monthly water requirements based on a daily water balance across the entire country. The result is the generation of 10 x 10 m raster layers related to water use in irrigated crops, which can be accessed through its GIS web portal (www.espaciosiar.es).
Temporal scale	Short term
Update frequency	Real time. The stations record temperature and humidity values ​​every 10 minutes and radiation, precipitation, and wind speed and direction every 10 seconds, generating average hourly and daily records. Data is provided to users in the form of daily, weekly and monthly logs; average hourly logs are also provided.
Spatial resolution	10 x 10m
Geographical coverage	Spain
Limitations and caveats	N/A
Additional notes	Irrigator Advisory Offices have been set up in each autonomous community, managed by the respective regional administrations, which contribute to the dissemination of data – including data from the SiAR – providing the public with information on crop water requirements, irrigation programmes and other data of agronomic interest.

Source: (MAPA, n.d.[135]).

Water use and consumption monitoring

Table A A.13. OpenET

Developed by	Public-private collaboration led by NASA, Environmental Defense Fund, Desert Research Institute, Google Earth Engine, HabitatSeven and several universities, with input from more than 100 stakeholders.
Type(s) of physical water risk covered	Water shortage (green water – evapotranspiration)
Dimensions of risk included	Exposure, vulnerability.
Purpose	Monitor crop water consumption by providing satellite-based information on evapotranspiration (ET).
Intended user	Farmers, water managers
Format	Online data explorer with maps of ET and the FARMS tool (Farm and Ranch Management Support), a user-friendly interface that can generate customisable reports.
Data source	Relies on publicly available satellite, meteorological, land use and soil data as inputs to the ET models: Landsat is the primary satellite dataset; also uses weather station measurements to produce spatially distributed or gridded weather datasets; ancillary datasets come from the U.S. Department of Agriculture (USDA) (e.g. crop type, soils), the U.S. Geological Survey (USGS) and state agencies.
Methodology/technology	Uses open-source models and Google Earth Engine: OpenET uses a multi-model ensemble approach, combining results from several ET models, using inputs with satellite imagery, weather data and land surface information.
Temporal scale	Short-term
Update frequency	Daily, monthly and yearly intervals
Spatial resolution	Finest resolution can be a quarter of an acre
Geographical coverage	Western United States
Limitations and caveats	Varying accuracy across different models, regions and land cover types, e.g. OpenET models are systematically biased high in evergreen and mixed forests and have lower accuracy in shrublands and grasslands. Results are also affected by quality and availability of satellite data, e.g. small regions of missing satellite data. The OpenET website discloses the tool’s limitations under “Accuracy & Known Issues”.

Source: (NASA, 2021[136]); (OpenET, 2026[137]).

Table A A.14. WaPOR

Developed by	FAO
Type(s) of physical water risk covered	Water shortage; green water (evapotranspiration)
Dimensions of risk included	Hazard, exposure, vulnerability
Purpose	Monitoring of agricultural water productivity at different scales; other applications include monitoring crop water consumption, stressor impact on agriculture (e.g. drought); water accounting, monitoring irrigation performance…
Intended user	Extension services, policymakers, water and irrigation managers,
Format	Publicly accessible near real-time database using satellite data
Data source	Data derived from open-access remote-sensing data and open-source algorithms. CHIRPS Precipitation, Copernicus DEM, (Ag)ERA5 Meteorological Data, GEOS-5 Meteorological Data, IMERG Precipitation, Landsat satellites, MODIS sensors, MSG satellites, Sentinel-2 satellites, VIIRS sensors and WorldCover Land Cover.
Methodology/technology	Satellite/remote sensing data.
Temporal scale	Near real time information with a temporal coverage from 2009 to the present.
Update frequency	WaPOR data is made available at different frequencies, depending on the data layer and the resolution in question: annual, seasonal, monthly, every 10 days, daily. For example, Actual Evapotranspiration and Interception is made available annually, monthly and every 10 days at all resolutions; Land Cover Classification is made available every 10 days at 20m resolution and annually at 100m and 200m. Gross Biomass Water Productivity is made available seasonally.
Spatial resolution	3 different levels corresponding to different resolutions at which different applications for the data are possible: Level 1 (global) = 300m resolution; Level 2 (continental and national) = 100m; Level 3 (irrigation scheme and sub-basin) = 20m
Geographical coverage	The global level (300m resolution) covers the entire globe; the continental and national / river basin level (100 m ground resolution) covers Northern and sub-Saharan Africa and the Near East (roughly a square of -30W, -40S, 65E, 40N); the irrigation scheme and sub-basin (20 m ground resolution) is available in 36 areas as of July 2024 (latest information available on website).
Limitations and caveats	Known limitations of remotely sensed data including coarse spatial resolution for some variables; cloud interference; land cover noise; inaccurate estimation of transpiration (T) and evaporation (E) depending on land use classes.

Source: (FAO, 2026[138]).

Table A A.15. EEFLUX

Developed by	Consortium of researchers and Google Inc.
Type(s) of physical water risk covered	Water shortage
Dimensions of risk included	Exposure, vulnerability; green water (evapotranspiration)
Purpose	Produce field-scale maps of water consumption to track agricultural water use.
Intended user	Water managers
Format	Mobile app
Data source	Landsat satellite images; North American Land Data Assimilation System hourly gridded weather data collection for energy balance calibration and time integration of ET; Reference ET is calculated using the ASCE (2005) Penman-Monteith and GridMET weather data sets; Statsgo soil data base of the USDA provides soil type information.
Methodology/technology	Surface energy balance model “METRIC” (Mapping ET at high Resolution with Internalized Calibration) drawing on the Google Earth Engine which gives access to Landsat image collection and in conjunction with access to a suite of gridded weather system data.
Temporal scale	Short term. In addition, given that Landsat satellites have collected thermal data since 1984, water consumption under existing conservation practices can be compared with those occurring in the past 40 years.
Update frequency	Near real-time.
Spatial resolution	Field-scale / 30m.
Geographical coverage	Continental United States.
Limitations and caveats	Studies have found large ET overpredictions and errors of up to 181% (Kadam et al., 2021[88]).

Source: (Allen et al., 2019[139]).

Digital twins

Table A A.16. Digital Twin of the Limpopo River Basin

Developed by	CGIAR/IWMI
Type(s) of physical water risk covered	Water shortage, water excess, water quality
Dimensions of risk included	Hazard
Purpose	The digital twin houses several tools, including: a drought monitor; an irrigation mapping tool; river discharge & environmental determination tool; reservoir volume monitoring; reservoir forecasting; water quality monitoring.
Intended user	Limpopo Watercourse Commission (LIMCOM), established between the Republics of Botswana, Mozambique, South Africa and Zimbabwe to support transboundary water management.
Format	The Digital Twin Portal is the central hub for accessing and interacting with the digital twin model. An interactive tool for visualising datasets through graphics and maps enables exploration of spatial and temporal trends in water resources and an AI-virtual assistant allows users to interact with datasets through a natural language chat interface.
Data source	305 discharge stations provided by South Africa’s Department of Water and Sanitation. 303 rainfall stations based on CHIRPS data and incorporates three types of forecasts, supported by a 20-year historical database.
Methodology/technology	The foundation of the digital twin is a SWAT hydrological model running in near real time, including a three-month seasonal forecast. River discharge & environmental flow estimates underpinned by PROBFLO, an environmental flow determination tool, Machine learning is used in the irrigation mapping and reservoir monitoring and forecasting tools. Water quality monitoring uses FISHTRAC, where sensors are attached to fish to gather real-time data on river health and water quality that triggers water pollution alerts.
Temporal scale	Short and medium term.
Update frequency	Near real time for some variables.
Spatial resolution	Varies; as fine as 10m for irrigation mapping.
Geographical coverage	Limpopo River Basin, Republics of Botswana, Mozambique, South Africa and Zimbabwe. A total of 1 408 channels covering 400 000 km² are available for seasonal forecasting.
Limitations and caveats

Source: (CGIAR/IWMI, 2024[140]).

Crop monitoring and forecasting tools

Table A A.17. Canadian Crop Metrics application

Developed by	Agriculture and Agri-Food Canada Forecasts are jointly produced by Agriculture and Agri-Food Canada and Statistics Canada
Type(s) of physical water risk covered	Water quantity (rainfall, soil moisture)
Dimensions of risk included	Impact (yields)
Purpose	Early warning information on crop yield and production. The application allows users to examine in-season weather, satellite-based and risk data impacting crop yields in Canada alongside historical and forecasted crop yields.
Potential users	Producers, grain traders, transporters and government policymakers for market access and food security planning
Format	Web application: Interactive website with maps. The Canadian Crop Metrics application allows users to look at specific regions and generate reports, graphs and tables to compare current conditions to historical conditions for 14 different crop types.
Data source	Historical yield data from climate and satellite data as inputs. The ICCYF integrates climate, remote sensing derived vegetation indices, soil and crop information through a physical process-based soil water budget model and statistical algorithms.
Methodology/technology	Forecasted yield comes from the Canadian Crop Yield Forecaster, a regional crop yield forecasting tool which models yields of major crops in Canada from a statistical model trained on historical yield from Statistics Canada. The model integrates climate, remote sensing derived vegetation indices, soil and crop information through a physical process-based soil water budget model and statistical algorithms. Production numbers are arrived at by weighting the modelled crop yield forecast against Statistics Canada’s estimates of the harvest area from the June or July survey whenever it is available. For regions where model-based estimates were not available because of insufficient input data to build and train the model, mean values of the previous five years of farm survey data are used.
Temporal scale	Medium-term: Seasonal
Update frequency	Weather data is updated regularly and yield estimates are updated monthly from July to October. Forecasts are made at the beginning of the months of July, August and September for all crops and an additional forecast is made for corn and soybeans (late season crops) at the beginning of October.
Spatial resolution	Census agricultural region (CAR). CARs are used by the Census of Agriculture to disseminate agricultural statistics.
Geographical coverage	Canada (CAR, province and national levels).
Limitations and caveats	Model performance is better from regions with a good coverage of climate stations and a high percentage of cropped area. The CAR is a relatively coarse unit that may contain several soil and climate zones, limiting the predictive power of the model; forecasts at sub-CAR levels is limited by a lack of yield data at finer scales.
Additional notes	The information from Canadian Crop Metrics feeds into the GEOGLAM Crop Monitor global forecasting tool.

Source: (Government of Canada, 2025[141]) (Chipanshi et al., 2015[142]).

Table A A.18. Agricultural Stress Index System (ASIS)

Developed by	FAO, in collaboration with the Joint Research Centre of the European Commission (JRC), the Flemish Institute for Technological Research (VITO) and International Institute for Geo-Information Science and Earth Observation (ITC) of the University of Twente, Netherlands
Type(s) of physical water risk covered	Water shortage (drought) Blue and green water
Dimensions of risk included	Drought hazard, exposure and impact: Indicate agricultural areas (cropland and grassland) under water stress
Purpose	Drought monitoring
Intended user	International organisations, Ministries of Agriculture, Ministry of Trade, insurers, disaster emergency managers, agri-food companies, international development and aid agencies
Format	Openly accessible platform with map
Data source	Historical data on cropping cycles, remote sensing data of vegetation and land surface temperature
Methodology/technology	The index is calculated based on remote sensing data of vegetation health, combined with information on agricultural cropping cycles (historical data), taking into account the duration and intensity of drought.
Temporal scale	10 days
Update frequency	3 times per month
Spatial resolution	1 km
Geographical coverage	Global
Limitations and caveats	N/A
Additional notes	Won the 2016 Geospatial World Excellence Award. In April 2024, ASIS was recognised as a Digital Public Good (DPG) by Digital Public Goods Alliance.

Source: (FAO, 2025[143]), (FAO, 2025[144]), (ITC, n.d.[145]).

Table A A.19. GEOGLAM Crop Monitor

Developed by	NASA Harvest
Type(s) of physical water risk covered	Water shortage (droughts), excess (floods) Blue and green water (rainfall, soil moisture)
Dimensions of risk included	Hazard, exposure, vulnerability; impact on crop yields, food security.
Purpose	Agricultural monitoring: Open and timely information on crop conditions in support of market transparency and early warning of production shortfalls.
Intended user	International organisations, Ministries of Agriculture, Ministry of Trade, national agencies responsible for food security policy and response programmes/ disaster emergency managers, insurers, agri-food companies, international development and aid agencies
Format	Online crop monitor exploring tools (interactive maps), synthesis maps, agrometeorological indicators, accompanied by monthly crop monitor bulletins and supplemental reports.
Data source	Earth observation satellite data (NOAA, CHIRPS, NASA MODIS…)
Methodology/technology	Partner organisations submit crop condition information using the common crop condition classification system using the web-based interface based on their own data sources and decision support systems. The web-based interface provides up to date key EO data products for agricultural monitoring along with crop condition plots of EO data, and contextual information on crop spatial extent (Crop Masks) and crop growth stages (Crop Calendars) at the sub-national level.
Temporal scale	Medium term, seasonal
Update frequency	Monthly
Spatial resolution	National and sub-national (regional) level information
Geographical coverage	>97% of the world’s croplands
Limitations and caveats	N/A
Additional notes	N/A

Source: (Crop Monitor, 2026[146])

Hazard and risk maps

Water excess

Table A A.20. IWMI Flood Risk Mapping

Developed by	IWMI
Type(s) of physical water risk covered	Water excess (flood) Blue Water
Dimensions of risk included	Hazard, Exposure (changes in population, land use and shifting climatic patterns), Vulnerability (e.g. where people are vulnerable to floods) and Impact (Where crop losses can occur)
Purpose	Anticipation: estimate the extent and dynamics of flood inundation
Intended user	Ministries of Agriculture/Environment/Water, local and regional governments, disaster emergency managers, river basin managers, environment/ water agencies, insurers, farmers
Format	Web-based mapping tool and dataset
Data source	Satellites and airborne instruments (MODIS satellite data and data from optical sensors)
Methodology/technology	Maximum flood inundation extent is estimated using an estimation algorithm.
Temporal scale	8-day, monthly maximum inundation extent, seasonal (duration of inundation cycles)
Update frequency	N/A
Spatial resolution	500 m
Geographical coverage	South Asia, Southeast Asia and Nigeria.
Limitations and caveats	N/A

Source: (IWMI, 2025[147]), (IWMI, 2025[148]).

Water shortage

Table A A.21. Aqueduct Food

Developed by	World Resources Institute (WRI)
Type(s) of physical water risk covered	Water shortage (water stress; water depletion; drought risk; interannual variability, groundwater table decline, etc.) Blue water
Dimensions of risk included	Hazard and exposure
Purpose	Anticipation. Identifies current and future water risks to agriculture and food security. Aqueduct Food combines global data on water risks and agriculture to illustrate water-related threats to and opportunities for food security, and how these dynamics may develop over time. The tool allows users to see how changes in climate and demand for water could affect their food‐producing areas
Intended user	Ministries of water/environment and agriculture, agri-food companies, international development organisations (e.g. development banks, international aid agencies)
Format	Web-based mapping tool
Data source	Diverse, including modelled data
Methodology/technology	The tool combines water data from WRI Aqueduct (e.g. drought risk)) with food data from the International Food Policy Research Institute (IFPRI) (e.g. crop production, market and trade data, data on hunger). Modelling approaches help show water risks for crops.
Temporal scale	Baseline (present day and past, dependent on the dataset, 2030 and 2050.
Update frequency	Unknown
Spatial resolution	10km2
Geographical coverage	Global
Limitations and caveats	The datasets combined (WRI Aqueduct and IFPRI) were generated with different inputs and modeling approaches; Limitations of the datasets used as inputs; Different years from which baseline data is derived from, limited data availability; There may be more variation in the risk scores in each region, compared to what aggregated results show in the tool; Modelling uncertainties; No data on pastureland and livestock; The vulnerability of crops to water risks is not modelled; Food availability is estimated based on food demand only. Other influencing factors, e.g. price, are not modelled.

Source: (WRI, 2021[149]).

Table A A.22. European Drought Risk Atlas

Developed by	CIMA Research Foundation, Vrije Universiteit Amsterdam, the European Commission’s Joint Research Centre
Type(s) of physical water risk covered	Water shortage (drought) Blue and green water (e.g. soil moisture, groundwater)
Dimensions of risk included	A systematic overview of current and future drought risk (considering hazard, vulnerability and exposure) as well as impact.
Purpose	Anticipation Mapping and understanding current drought risk and forecasting future drought risk under different levels of global warming (+1.5°C, +2°C, +3°C).
Intended user	The tool covers several sectors, including agriculture. Ministries of Agriculture/Environment/Water, Agro-food companies, international development/aid agencies, local/regional governments, river basin managers, extension officers
Format	Static publication (published in 2023)
Data source	For agriculture, crop yield data, combined with modelled data from the Regional Climate Models of the EURO-CORDEX initiative under the RCP 4.5 and 8.5 scenarios.
Methodology/technology	Impact-based drought assessment characterising how drought hazard, exposure and vulnerability interact and affect different but interconnected systems/sectors. It uses i) impact chains (sector/system specific conceptual models of drought risk built based on a European literature review and expert consultations visualising risk drivers and their interactions determining impact) and ii) the quantitative data-driven assessment of sectoral drought risk based on machine learning models and the available data (both historical climate data and data from future-looking climate and hydrological models). Socio-economic evolution and technological development are not considered. Based on the information from impact chains, vulnerability factors and exposure are assessed at European level. Subsequently, European regions are clustered based on their vulnerability to drought risks. Machine learning models are then trained to learn the relationship between impact and drought hazard per vulnerability cluster, based on which risk is estimated (i.e. the likelihood of experiencing certain impacts) and summarised into the average annual loss risk metric (i.e. annual drought-induced impact). This metric is represented as relative production loss of region-specific production and combined with exposure data, estimated average annual and probable maximum production losses can be calculated.
Temporal scale	Current and future drought risk (time is not specified, only the level of farming)
Update frequency	N/A
Spatial resolution	NUTS-2 regions
Geographical coverage	European Union
Limitations and caveats	Socio-economic developments are not covered which can impact the reliability of future projections. Drought impact data can be sparse and limited, enhancing uncertainty.

Source: (JRC, 2023[90]).

Table A A.23. World Drought Atlas

Developed by	The European Commission’s Joint Research Centre, UNCCD, CIMA Research Foundation, United Nations University and Vrije Universiteit Amsterdam
Type(s) of physical water risk covered	Water shortage (drought) Blue and green water (e.g. soil moisture)
Dimensions of risk included	A systematic overview of current and future drought risk (considering hazard, vulnerability and exposure) and impact. Cascading impacts and cross-sectoral risks are considered as well.
Purpose	Mapping and understanding current drought risk and forecasting future drought risk under different levels of global warming (+2°C, +3°C, +4°C) and scenarios of socio-economic development.
Intended user	Ministries of Agriculture/Environment/Water, Agro-food companies, international development/aid agencies, local/regional governments, river basin managers, extension officers The tool covers several sectors, including agriculture.
Format	Static publication (published in 2024), including maps and graphs to conceptualise drought risk
Data source	data on past droughts (e.g. Standardized Precipitation-Evapotranspiration Index during past droughts, modelled data (e.g. data from climate scenarios and socio-economic pathways, including population trends).
Methodology/technology	Impact-based drought risk assessment: impact chains, i.e. conceptual models outlining the main drought drivers for specific sectors and their impacts, highlighting interconnections and dependencies that need to be considered to reduce drought risk.
Temporal scale	Varies (current drought risk, past impact and long-term risks depicted)
Update frequency	N/A
Spatial resolution	N/A
Geographical coverage	Global
Limitations and caveats	N/A
Additional notes	The atlas aims to provide policymakers with an understanding of drought risk, including the ways in which it affects critical sectors, providing examples through case studies of past events and future-looking projections based on modelled socio-economic and climatic data. The atlas is not comprehensive but aims to provide policymakers with examples of actions to take to help reduce and manage drought risk proactively.

Source: (JRC and UNCDD, 2024[150]).

Table A A.24. United States Drought Risk Atlas

Developed by	National Drought Mitigation Center together with national agencies
Type(s) of physical water risk covered	Water shortage (drought) Blue water
Dimensions of risk included	Drought hazard (drought frequency, intensity, duration and magnitude)
Purpose	Anticipation/foresight (based on historical data). Explore historic trends in various drought indicators from thousands of weather stations.
Intended user	River basin and irrigation managers, environment/water agencies, local/regional government, ministries of agriculture/environment, insurers, extension officers
Format	Interactive web-based platform, with maps, graphs and tables. It provides pre-computed drought indices for more than 4 000 locations across the United States, including e-generated heat maps, time series, tabular analyses and weekly Standardized Precipitation Indices, Standardized Precipitation and Evapotranspiration Indices, Palmer Drought Severity Indices, self-calibrated Palmer Drought Severity Indices, Standardized Streamflow Indices.
Data source	Historical data from weather stations that have at least 40 years of record.
Methodology/technology	Regional frequency analysis conducted on historical data, using L-moments which allows to combine observation series from different stations determining a common distribution over a larger area, ensuring that outliers from the historical record do not distort final results.
Temporal scale	Historical data spanning at least 40 years
Update frequency	Varies, weekly for some indices
Spatial resolution	Data from specific weather stations
Geographical coverage	United States
Limitations and caveats	N/A
Additional notes	N/A

Source: (National Drought Mitigation Center, 2025[151]) (Svoboda et al., 2015[152]).

Table A A.25. Latin American and Caribbean Drought Atlas

Developed by	CAZALAC (Water Centre for Arid Zones), UNESCO, US Army Corps of Engineers and JRC
Type(s) of physical water risk covered	Water shortage (drought) Blue water
Dimensions of risk included	Drought hazard (frequency)
Purpose	Anticipation
Intended user	River basin and irrigation managers, environment/water agencies, local/regional government, ministries of agriculture/environment, insurers, extension officers
Format	Web-based platform, with maps, graphs and tables. Maps of drought frequency (with return periods associated to drought intensities), minimum expected rainfall amount associated to certain return periods, maximum expected rainfall associated to certain return periods.
Data source	Historical data based on weather stations
Methodology/technology	Regional frequency analysis using L-moments (see above for the United States Drought Risk Atlas) to determine the common distribution of observation series over larger areas
Temporal scale	N/A
Update frequency	N/A
Spatial resolution	Country-level maps, with data extrapolated from weather stations
Geographical coverage	Latin America and the Caribbean
Limitations and caveats	N/A
Additional notes	N/A

Source: (Núñez Cobo and Verbist, 2018[153]) (Núñez J. and Verbist, n.d.[154]).

Water quality and risk of undermined freshwater ecosystem resilience

Table A A.26. WWF Water Risk Filter

Developed by	WWF
Type(s) of physical water risk covered	Water quality, risk of undermined freshwater ecosystem resilience (Water excess and water shortage are mapped too, but this table focuses on water quality and the risk of undermined freshwater ecosystem resilience. Some of the filters the tool uses for measuring the former too are covered under tools above)
Dimensions of risk included	Hazard and exposure (population density and GDP) Hazards mapped: Water quality: nitrate-nitrite concentration, periphyton growth potential, toxicity stress, mismanaged plastic waste, risk of pesticide pollution, total dissolved solids and surface water quality Ecosystem service status: Catchment ecosystem services degradation level, violation of environmental flows, wetland degradation, invasive species, river extent change, fragmentation status of rivers
Purpose	Anticipation
Intended user	Ministries of agriculture/environment, environment/water agencies, local/regional government and the private sector
Format	Web-based maps
Data source	Historical observed data (within a range of mostly from the early 2000s to 2022) and modelled data
Methodology/technology	Nitrate-nitrite concentration: Annual average nitrogen in rivers, based on observed monitoring data and a machine learning prediction model, predicting values between 2006-2010. Periphyton Growth Potential: Global modelling of dissolved and total nitrogen (N) and phosphorus (P) concentrations and ratios that can limit periphyton proliferation during the growing season. Toxicity stress: pressure of ecological impact using predicted (modelled) environmental concentrations of 1 785 chemicals Mismanaged plastic waste; risk of pesticide pollution: modelled data Total Dissolved Solids: Salinity between 2010-2019 based on the results of the dynamical surface water quality model Surface Water Quality Index BOD: Shows biological oxygen demand (BOD) based on a dynamical surface water quality model of 2010-2019. Surface Water Quality Index Electrical Conductivity: A proxy indicator for salinity based on the Electrical Conductivity (EC) parameter of the World Bank's Environmental Water Quality Index, mapped based on machine learning models using remote sensing and monitoring station data from 2000-2010. Global Forest Change: Tress cover loss proxying catchment ecosystem services degradation, using Landsat data from 2000‑2022. Violation of Environmental Flows: modelling using discharge data and environmental flow envelopes Wetland degradation: Maps of wetland loss between 1700-2020 based on drainage records, wetland conversion records, maps of land-use and land-use change, and simulated wetland extent. Invasive species: Observed data from the Invasive Species Specialist Group’s Global Invasive Species Database River extent change: based on Landsat imagery Fragmented status of rivers: An integrated connectivity status index (CSI) calculated using a geometric network of the global river system and its hypergeometric properties.
Temporal scale	N/A
Update frequency	N/A
Spatial resolution	N/A
Geographical coverage	Global
Limitations and caveats	N/A

Source: (WWF, 2025[155]).

Hydrological infrastructure maps

Table A A.27. Agricultural Drainage Database (“BD Drainage”)

Developed by	L’Office français de la biodiversité (OFB), le Bureau de Recherche Géologique et Minière (BRGM), l'Association de recherche sur le Ruissellement, l’Érosion et l’Aménagement du Sol (AREAS). Original pilot involved Seine Normandy Water Agency, Eure and Seine-Maritime Departmental Councils.
Type(s) of physical water risk covered	Water excess; water quality.
Dimensions of risk included	Exposure, vulnerability.
Purpose	Provides information on the path taken by water from agricultural plots that have been drained, through to its discharge into the natural environment, watercourses and groundwater. The enrichment of the drainage database with related services makes it a useful decision-making tool for water and risk management and resource protection.
Intended user	Local authorities; engineering design firms.
Format	Interactive mapping tool
Data source	Survey of sub-national authorities and chambers of agriculture who hold data on local drainage infrastructure.
Methodology/technology	Publicly available database contains map layers, services showing upstream and downstream paths and more detailed technical data sheets.
Temporal scale	Long-term
Update frequency	n/a
Spatial resolution	Plot level
Geographical coverage	Initially applied in the Normandy region of France, the database is being extended nationally.
Limitations and caveats
Additional notes

Source: (BRGM, 2019[156]).

Impact assessment tools

Water shortage

Table A A.28. Australian Agricultural Drought Indicators *

Developed by	Australian Bureau of Agricultural and Resource Economics (ABARES) in partnership with the Commonwealth Scientific and Industrial Research Organisation (CSIRO), with input from the Queensland Department of Environment and Science (DES)
Type(s) of physical water risk covered	Water shortage (drought) Blue water
Dimensions of risk included	Hazard, vulnerability, exposure and impact.
Purpose	Drought anticipation and monitoring: estimate anticipated agricultural and economic impacts for Australian broadacre agriculture (non-irrigated extensive livestock and cropping)
Intended user	Staff of the Australian Government
Format	A user interface is in preparation.
Data source	Observed and forecasted weather data, as well as data and assumptions on the types of soil, pasture and agricultural activity, including farm survey data. For observed data, the model uses data from the past 33 years on a rolling basis.
Methodology/technology	Outcome/impact-based drought forecasting using biophysical and economic modelling. The bio-physical and economic modelling system translates climate observations and forecasts to outcome-based indicators of crop yields, pasture growth and farm business profits. The models include machine learning. The modelling system builds on and integrates input from other existing Australian models, including pasture growth via the AussieGRASS system and the GrassGro model, winter and summer crop yields via APSIM and farm business profits via the farmpredict model (see above).
Temporal scale	Annual (data is shown for the Australian financial year, running from July to June)
Update frequency	Monthly, using observed data from the past months and forecasted for the rest of the year
Spatial resolution	5 km Aggregates at national, state or local government areas can be produced as well, with weightings to consider the relative amount of agricultural activity.
Geographical coverage	Australia, for defined agricultural zones (areas with no agricultural activity, e.g. forestry, protected areas, are excluded)
Limitations and caveats	The tool is in an experimental/prototype phase.
Additional notes	N/A

Source: (ABARES, 2024[157]) (Hughes et al., 2025[93]).

Table A A.29. Drought Impact Assessment Platform (d-iap)

Developed by	FAO
Type(s) of physical water risk covered	Water shortage
Dimensions of risk included	Hazard, exposure, vulnerability
Purpose	Evaluate drought impacts on crop and water productivity as well as irrigation water requirements under present and future climate scenarios.
Intended user	Farmer advisory services, water authorities and managers, policymakers, insurers
Format	Web-based tool
Data source	Global Land Cover - SHARE (GLC-SHARE) database from FAO; climate data from European Centre for Medium-Range Weather Forecasts (ECMWF); projected climate data for the future scenarios from the database of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP); soil data from Harmonized World Soil Database version 2.0; among others.
Methodology/technology	Integrates diverse methodologies and tools, including the AquaCrop crop simulation models.
Temporal scale	Present: 1960-2022 (63 years); Future: 2040-2059 (Mid-century); 2080-2099 (End-century).
Update frequency	n/a
Spatial resolution	9km grid square (0.1°x 0.1°)
Geographical coverage	Global
Limitations and caveats	Does not yet take into account: sub-national cropping patterns; crop cycle or varieties; rooting depth; groundwater, soil and water salinity and other stressors affecting outcomes.
Additional notes	Covers 16 crops; rainfed and irrigated systems;

Source: (FAO, 2026[158]).

Table A A.30. ABARES farmpredict

Developed by	Australian Bureau of Agricultural and Resource Economics (ABARES)
Type(s) of physical water risk covered	Water shortage (drought) Blue and green water
Dimensions of risk included	Hazard, vulnerability and exposure to weather related risks, particularly drought
Purpose	Anticipate physical and financial outcomes for Australian farms based on weather and climatic conditions and commodity prices. Potential modelled outcomes include: farm financial performance (e.g. farm business profit, farm cash income, rate-of-return), production and revenue (of various crop and livestock types), input use and cost (e.g. fertiliser, fuel) and change in stock. The model is used for forecasting, drought monitoring and climate scenario modelling.
Intended user	ABARES
Format	Model (not publicly accessible)
Data source	Climate and weather data (rainfall, soil moisture, temperature, frost, heat extremes); Price data (input and output prices), and farm-specific information (e.g. location, size, livestock, grain stock, machinery, etc.) based on data from the Australian Agricultural and Grazing Industry Survey (AAGIS).
Methodology/technology	A non-parametric machine-learning micro-simulation model using a gradient boosted regression tree algorithm.
Temporal scale	N/A
Update frequency	N/A
Spatial resolution	Farm level
Geographical coverage	Australia (national coverage)
Limitations and caveats	N/A
Additional notes	N/A

Source: (ABARES, 2025[159]) (Hughes et al., 2022[160]).

Water resource assessment tools

Table A A.31. California Water Watch

Developed by	California Department of Water Resources
Type(s) of physical water risk covered	Water shortage Blue water
Dimensions of risk included	Hazard
Purpose	Water supply monitoring and forecasting Monitor precipitation, temperatures, groundwater, reservoir and snowpack levels, streamflow, soil moisture and vegetation conditions. Provide short-term forecasts on these variables for river basins to make real-time decisions on water rights allocations.
Intended user	Water management agencies
Format	Freely accessible online portal with an interactive interface, providing maps and graphs
Data source	Observed, forecasted and remotely sensed water and weather data, as well as modelled information (e.g. water supply forecasts for river basins)
Methodology/technology	Remote sensing and GIS mapping, hydrological modelling and weather forecasting
Temporal scale	Monitoring information: daily Forecasts: 6-10-day, 30-day and 90-day outlook
Update frequency	Most information is updated daily
Spatial resolution	N/A
Geographical coverage	The State of California, United States
Limitations and caveats	N/A

Source: (California Water Watch, n.d.[161]).

Table A A.32. Canada1Water

Developed by	Natural Resources Canada (Geological Survey of Canada) and Aquanty Inc. with additional contributions from Agriculture and Agri-Food Canada
Type(s) of physical water risk covered	Water quantity (blue and green water resources); Destabilised hydrological cycle
Dimensions of risk included	Hazard
Purpose	Provide historical and forward-looking projections to the middle and end of the 21st century for: Water resources (Groundwater and surface water levels, flows and trends); Land surface (Evapotranspiration and potential evapotranspiration, snow depth and density, soil moisture); Climate change (Regional simulations of climate patterns including large lake effects for maximum accuracy).
Intended user
Format	An online decision support system platform will provide a window to the model results and provide information accessible to the nontechnical user at public, community, watershed management level, and higher governmental levels. The second phase of Canada1Water due to run to 2029 aims to: Develop national groundwater metrics Deliver total water storage metrics based on Gravity Research and Climate Experiment (GRACE) satellite data Partition those total water storage metrics to support more accurate groundwater estimates Provide complete water budgets — for both groundwater and surface water — by watershed across Canada
Data source	HGS is a physical-based model calibrated using more than 400 Environment Canada and U.S. Geological Survey stream-gauging locations and more than 3 000 groundwater monitoring wells. Model draws on 20 input datasets.
Methodology/technology	Canada1Water is a continental-scale model of Canada’s complete hydrologic system based on the HydroGeoSphere platform. Canada1Water also models regional climate, using the Weather Research and Forecasting (WRF) model for climate and the Community Land Model (CLM) for near surface processes associated with the energy balance between the land surface and atmosphere. The outputs generated by WRF feed into CLM5, and the combined output of WRF and CLM5 feeds into HGS, which serves as the engine for the final, integrated groundwater– surface water simulation of Canada’s water resources.
Temporal scale	Medium to long-term: The outputs provide insight into projected changes in groundwater storage, soil moisture levels, surface water flows, evaporation and plant transpiration in monthly increments between historic and future conditions.
Update frequency	n/a
Spatial resolution	The WRF and CLM models have respective grid resolutions of 12.5 km and 5 km, and outputs are integrated to monthly temporal increments. HGS groundwater-surface water modelling utilises unstructured model grids constructed to resolve Strahler Order 5 and 4 stream networks with length scales of < 1km to 5km.
Geographical coverage	Canada plus transboundary watersheds shared by Canada and the United States.
Limitations and caveats	N/A
Additional notes	N/A

Source: (Canada1Water, n.d.[162]) (Frey, Russell and Kirkwood, 2024[163]).

Projection tools for long-term trends

Table A A.33. CANARI-Europe

Developed by	French laboratory Institut Pierre Simon Laplace (climate modelling) and the JRC (adaptation in the agriculture sector) providing research, Makina Corpus providing the IT solutions for the platform and Solagro, the presentation of results towards end users
Type(s) of physical water risk covered	Water excess and shortage Blue water
Dimensions of risk included	Hazard and impact on different crops/livestock sectors
Purpose	Anticipation: calculate over 100 agroclimatic indicators considering local agroclimatic conditions The tool is developed specifically for the agriculture sector.
Intended user	Ministries of Agriculture, Water and Environment, Local and regional authorities, agro-food companies, farmers, extension officers, environment and water agencies, river basin managers, disaster preparedness and risk reduction, insurers
Format	free, openly accessible web-based decision-support platform: showing maps, charts, summary tables, summary reports and data exports
Data source	Modelled data from Regional Climate Models of the EURO-CORDEX initiative, using medium and high-emissions (RCP 4.5 and 8.5) scenarios, historic agroclimatic data
Methodology/technology	Statistical and dynamic downscaling of climate models, incorporating agricultural data (e.g. yields)
Temporal scale	1985 and 2100. Each agroclimatic indicator can be calculated and spatially mapped for the past, as well as the near (2020-2050) and far future (2050-2100) periods
Update frequency	Daily
Spatial resolution	12.5 km
Geographical coverage	Europe
Limitations and caveats	Advanced customisation requires external support. The tool requires intermediate technical knowledge for optimal use.
Additional notes	Besides water risks, the tool also covers agri-climatic indicators for non-water related risks. The tool is available in English, Spanish, Estonian, French. In France, CANARI Europe can be complemented by the Climadiag-Agriculture platform, integrating data from the French meteorological service (Météo-France) and phenological indicators that uses annually updated plant-growth models and 17 climate models. It can be integrated with several other tools, including the AWA AgriAdapt Webtool (see below), and the Climadiag-Agriculture France platform. It can also be combined with insurance and risk platforms for refining agricultural climate risk assessments and with National Meteorological Services for real time observations.

Source: (Climate-ADAPT, 2025[164]), (Solagro and Makina Corpus, 2025[103]), (Solagro and Makina Corpus, 2025[165]), (Solagro and Météo-France, 2025[166]).

Table A A.34. AgriAdapt Webtool for Adaptation

Developed by	Solagro, the Estonian University of Life Sciences, Fundacion Global Nature and Bodensee Stiftung
Type(s) of physical water risk covered	Water excess and shortage Blue water
Dimensions of risk included	Hazard and impact on different crops/livestock sectors
Purpose	Anticipation: assess the vulnerability of key European agricultural products to changing climatic patterns, including water related risks. Simulations show trends of several water related indicators (e.g. annual average rainfall, potential evapotranspiration) in the recent past and near future, as well as agroclimatic indicators for specific agricultural production sectors (e.g. hydric deficit for arable crops). The tool is developed specifically for the agriculture sector.
Intended user	Ministries of Agriculture, Water and Environment, Local and regional authorities, agro-food companies, farmers, extension officers, environment and water agencies, river basin managers, disaster preparedness and risk reduction, insurers
Format	Web-based tool including a map interface with overview on agronomic (yields) and climatic (observations and projections) data for different geographic locations
Data source	(past) yield data, as well as observed and projected (based on a moderate emissions scenario) climate data
Methodology/technology	Climate modelling simulations, modelling agri-climatic indicators for specific sectors
Temporal scale	recent past (past 30 years) and the near future (next 30 years)
Update frequency	N/A
Spatial resolution	N/A
Geographical coverage	Europe (specific farm locations across the continent)
Limitations and caveats	N/A
Additional notes	Besides water risks, the tool also covers agri-climatic indicators for non-water related risks. The tool is combined with a test to self-assess the vulnerability of farms to changing climatic patterns, including water-related risks, the tool allows farmers to understand climate-related risks. Complemented by a toolbox of potential measures, it provides a decision-support tool for agricultural stakeholders to plan climate change adaptation actions

Source: (Agri-Adapt, n.d.[104]).

Table A A.35. My Climate View

Developed by	Commonwealth Scientific and Industrial Research Organisation (CSIRO) and the Australian Bureau of Meteorology
Type(s) of physical water risk covered	Water excess and water shortage
Dimensions of risk included	Hazard and impact on agricultural sectors/commodities at specific locations
Purpose	Anticipate how climate change will impact farms, including for water related risk factors. A decision support tool providing information on past climate data, seasonal forecasts and future climate projections for specific locations for over 22 agricultural commodities
Intended user	Landholders, producers, agricultural advisers, Federal, state and territory governments
Format	Free, publicly accessible portal with a user interface, showing charts, summary tables, summary reports and data exports
Data source	Observed climate data, modelled climate data based on a moderate (RCP 4.5) and high emissions scenario (RCP 8.5)
Methodology/technology	Climate modelling for specific agricultural commodities and locations
Temporal scale	Past data from 1965 to now, seasonal forecasts for the next 1-3 month and future projections for the 2030s, 2050s and 2070s
Update frequency	N/A
Spatial resolution	5km grid
Geographical coverage	Australia
Limitations and caveats	N/A
Additional notes	The tool covers a broad range of climate risk relevant to agricultural commodities such as key seasonal changes in rainfall patterns, temperature and evapotranspiration.

Source: (MyClimateView, n.d.[105]).

Table A A.36. AquaPlan

Developed by	University of Manchester and Development Seed
Type(s) of physical water risk covered	Water shortage and water excess Blue water
Dimensions of risk included	Hazard, vulnerability, exposure and impact (on crops)
Purpose	Forecasting crop yield predictions under different water management strategies, taking into account soil, crop type and weather conditions The tool helps understand irrigation requirements, make field-level irrigation decisions and forecast climate change impacts on water-related crop production risks.
Intended user	The tool is specifically designed for the agriculture sector. Farmers, Ministries of Agriculture, Water and Environment, Local and regional authorities, agro-food companies, farmers, extension officers, environment and water agencies, river basin managers, disaster preparedness and risk reduction, insurers
Format	Interactive web application
Data source	cloud-hosted weather (Daymet, NASA-POWER) and soil (SoilGrids) datasets that are integrated into the application, combined with data generated from SSP scenarios and modelled data from the AquaCrop model
Methodology/technology	The app uses the “AquaCrop-OSPy” crop-water model which simulates how management decisions and climate variability affect crops. The model provides yield predictions under a variety of water management strategies, soil, crop type, and weather conditions. This is the open-source python version of the AquaCrop model, initially developed by FAO. Automated data integration and cloud processing enables to run the application.
Temporal scale	varies
Update frequency	N/A
Spatial resolution	Field-level
Geographical coverage	Global
Limitations and caveats	N/A
Additional notes	The app is intuitive and easy to use, compared to other water resource planning models that may be challenging to operate due to the complexity of uploading and managing data.

Source: (AquaPlan, n.d.[167]), (University of Manchester, 2024[168]), (Foster. T. et al., 2022[169]).